Dataset¶
In [1]:
from torch.utils.data import Dataset, DataLoader
import os
import pickle
from PIL import Image
import torch
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np
from EncoderCNN import Encoder
from ConvRNN import ConvLSTM
from Decoder import Decoder
from torchvision.utils import make_grid
import torch.optim as optim
from tqdm import tqdm
from MotionEncoder import MotionEncoder
from CameraEncoder import CameraEncoder
from RNN import EgoMotionRNN
from CameraDecoder import CameraDecoder
from DepthMap import DepthDecoder
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import random
from PIL import ImageDraw, ImageFilter
import torch.nn.functional as F
Transforms Functions for Dataset Loading¶
In [2]:
transform = transforms.Compose([
transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Resize((256, 512)),
transforms.ToTensor(),
])
In [3]:
def transform_segmentation_target(image):
"""
Transform a segmentation image to a tensor with class labels based on the red channel encoding.
"""
img_array = np.array(image, dtype=np.int64)
label = img_array[:, :, 0] # Extract the red channel as the label
# Map the red channel to class labels based on the dataset's specifications
label_map = {
0: 0, # Unlabeled
1: 1, # Building
2: 2, # Fence
3: 3, # Other
4: 4, # Pedestrian
5: 5, # Pole
6: 6, # RoadLine
7: 7, # Road
8: 8, # SideWalk
9: 9, # Vegetation
10: 10, # Vehicles
11: 11, # Wall
12: 12, # TrafficSign
13: 13, # Sky
14: 14, # Ground
15: 15, # Bridge
16: 16, # RailTrack
17: 17, # GuardRail
18: 18, # TrafficLight
19: 19, # Static
20: 20, # Dynamic
21: 21, # Water
22: 22, # Terrain
}
label_tensor = torch.from_numpy(np.vectorize(label_map.get)(label))
return label_tensor
In [4]:
target_transform = transforms.Compose([
transforms.Resize((256, 512)),
transforms.Lambda(lambda image: image.convert('RGB')),
transforms.Lambda(lambda img: transform_segmentation_target(img)),
])
In [5]:
depth_transform = transforms.Compose([
transforms.Resize((256, 512)),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
# Normalize depth values if necessary. This is just an example; adjust according to your depth data range.
#transforms.Normalize(mean=[0.5], std=[0.5])
])
In [6]:
# Define the root dataset path
root_dataset_path = '/home/user/asifa0/Project/CARLABase'
Functions to Corrupt BASE CARLA¶
In [7]:
def add_gaussian_noise(image):
row, col, ch = image.shape
mean = 0
var = 0.1
sigma = var ** 0.05
gauss = np.random.normal(mean, sigma, (row, col, ch))
gauss = gauss.reshape(row, col, ch)
noisy = image + gauss
noisy = np.clip(noisy, 0, 255)
return noisy.astype(np.uint8)
def add_clutter(image):
draw = ImageDraw.Draw(image)
number_of_shapes = random.randint(1, 5)
for _ in range(number_of_shapes):
x1, y1 = random.randint(0, image.size[0] - 1), random.randint(0, image.size[1] - 1)
x2, y2 = random.randint(0, image.size[0] - 1), random.randint(0, image.size[1] - 1)
draw.line((x1, y1, x2, y2), fill='white', width=2)
return image
def change_illumination(image):
new_image = image.astype(np.float64) * (0.6 + np.random.random() * 0.8)
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
def add_motion_blur(image, angle, radius):
# Apply motion blur to the image using PIL's ImageFilter.
image = image.filter(ImageFilter.GaussianBlur(radius=radius))
return image.rotate(angle, resample=Image.BICUBIC).filter(ImageFilter.GaussianBlur(radius)).rotate(-angle, resample=Image.BICUBIC)
def add_low_light_effect(image, factor):
# Multiply the image with a factor to simulate low light conditions.
new_image = image.astype(np.float64) * factor
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
def add_overexposure_effect(image, factor):
# Add a constant value to simulate overexposure.
new_image = image.astype(np.float64) + factor * 255
new_image = np.clip(new_image, 0, 255)
return new_image.astype(np.uint8)
Iterating over Directories, Loading Images and Creating Tensors¶
In [8]:
class SequentialImagesDataset(Dataset):
def __init__(self, root_dir, transform=None, target_transform=None, depth_transform=None, apply_corruption=False, include_towns=None, exclude_towns=None):
self.root_dir = root_dir
self.transform = transform
self.target_transform = target_transform
self.depth_transform = depth_transform
self.include_towns = include_towns
self.exclude_towns = exclude_towns
self.sequences = self._load_sequences()
self.apply_corruption = apply_corruption
def _load_sequences(self):
sequences = []
for town in sorted(os.listdir(self.root_dir)):
if self.include_towns and town not in self.include_towns:
continue
if self.exclude_towns and town in self.exclude_towns:
continue
town_path = os.path.join(self.root_dir, town)
if os.path.isdir(town_path):
for sequence in sorted(os.listdir(town_path)):
sequence_path = os.path.join(town_path, sequence)
if os.path.isdir(sequence_path):
# Filter files to include only those that match the expected pattern
valid_files = [f for f in os.listdir(sequence_path) if f.startswith('img_') and f.endswith('.png') and any(char.isdigit() for char in f)]
if valid_files:
# Sort the files based on the numeric part of the filename
images = sorted(valid_files, key=lambda f: int(''.join(filter(str.isdigit, f))))
images = [os.path.join(sequence_path, f) for f in images]
sequences.append(images)
return sequences
def __len__(self):
return len(self.sequences)
def __getitem__(self, idx):
image_paths = self.sequences[idx]
optical_flow_paths = [p.replace('img_', 'optical_flow_').replace('.png', '.npy') for p in image_paths]
segmentation_paths = [p.replace('img_', 'segmentation_') for p in image_paths]
depth_paths = [p.replace('img_', 'depth_') for p in image_paths]
sequence_tensors = []
segmentation_tensors = []
depth_tensors = []
optical_flow_tensors = []
# Load input Images and optical_flow files
for image_path, optical_flow_path in zip(image_paths, optical_flow_paths):
try:
img = Image.open(image_path).convert('RGB')
if self.apply_corruption:
img = np.array(img)
img = add_gaussian_noise(img)
# Overexposure effect
factor = random.uniform(0.1, 0.3)
img = add_overexposure_effect(np.array(img), factor)
img = Image.fromarray(img, 'RGB')
#img = add_clutter(img)
# Apply motion blur
angle = random.uniform(0, 0.5)
radius = random.uniform(2, 6)
img = np.array(img)
img = add_motion_blur(Image.fromarray(img), angle, radius)
img = np.array(img)
img = change_illumination(img)
img = Image.fromarray(img, 'RGB')
if self.transform:
img = self.transform(img)
sequence_tensors.append(img)
optical_flow = np.load(optical_flow_path)
optical_flow_tensor = torch.from_numpy(optical_flow).permute(2, 0, 1) # Assuming optical flow has shape (H, W, 2)
optical_flow_tensors.append(optical_flow_tensor)
except Exception as e:
print(f"Error loading image {image_path}: {e}")
sequence_tensors.append(torch.zeros((3, 256, 512)))
optical_flow_tensors.append(torch.zeros((2, 256, 512)))
# Load camera extrinsics for the sequence
meta_path = os.path.join(os.path.dirname(image_paths[0]), 'meta.pkl')
with open(meta_path, 'rb') as f:
meta_data = pickle.load(f)
extrinsics = torch.tensor(meta_data['extrinsics'][:6], dtype=torch.float32)
# Segmented images loading
for seg_path in segmentation_paths:
try:
with Image.open(seg_path) as seg:
if self.target_transform:
seg = self.target_transform(seg)
segmentation_tensors.append(seg)
except Exception as e:
print(f"Error loading segmentation image {seg_path}: {e}")
# Load depth maps
for depth_path in depth_paths:
try:
with Image.open(depth_path) as depth:
if self.depth_transform:
depth = self.depth_transform(depth)
depth_tensors.append(depth)
except Exception as e:
print(f"Error loading depth image {depth_path}: {e}")
sequence_tensor = torch.stack(sequence_tensors) if sequence_tensors else torch.zeros((6, 3, 256, 512))
optical_flow_tensor = torch.stack(optical_flow_tensors) if optical_flow_tensors else torch.zeros((6, 2, 256, 512))
depth_tensor = torch.stack(depth_tensors) if depth_tensors else torch.zeros((6, 1, 256, 512))
segmentation_tensor = torch.stack(segmentation_tensors) if segmentation_tensors else torch.zeros((6, 23, 256, 512))
return sequence_tensor, optical_flow_tensor, segmentation_tensor, extrinsics, depth_tensor
BASE CARLA¶
In [10]:
# Initialize the dataset for training, validation, and testing
org_train_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, exclude_towns=['Town02', 'Town10'])
org_val_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, include_towns=['Town02'])
org_test_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=True, include_towns=['Town10'])
In [11]:
# DataLoaders for each set
org_train_loader = DataLoader(org_train_dataset, batch_size=1, shuffle=False, num_workers=4)
org_val_loader = DataLoader(org_val_dataset, batch_size=1, shuffle=False, num_workers=4)
org_test_loader = DataLoader(org_test_dataset, batch_size=1, shuffle=False, num_workers=4)
Corrupted CARLA¶
In [12]:
# Initialize the dataset for training, validation, and testing
train_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, exclude_towns=['Town02', 'Town10'])
val_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, include_towns=['Town02'])
test_dataset = SequentialImagesDataset(root_dataset_path, transform=transform, target_transform=target_transform, depth_transform=depth_transform, apply_corruption=False, include_towns=['Town10'])
In [13]:
# DataLoaders for each set
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=4)
In [14]:
print(len(train_loader))
for sequence_tensor, optical_flow, segmentation_tensor, extrinsics, depth_tensor in train_loader:
print(sequence_tensor.shape)
print(optical_flow.shape)
print(segmentation_tensor.shape)
print(extrinsics.shape)
print(depth_tensor.shape)
break
print(len(org_train_loader))
for org_sequence_tensor, org_optical_flow, org_segmentation_tensor, org_extrinsics, org_depth_tensor in org_train_loader:
print(org_sequence_tensor.shape)
print(org_optical_flow.shape)
print(org_segmentation_tensor.shape)
print(org_extrinsics.shape)
print(org_depth_tensor.shape)
break
8982 torch.Size([1, 6, 3, 256, 512]) torch.Size([1, 6, 2, 512, 1024]) torch.Size([1, 6, 256, 512]) torch.Size([1, 6, 4, 4]) torch.Size([1, 6, 1, 256, 512]) 8982 torch.Size([1, 6, 3, 256, 512]) torch.Size([1, 6, 2, 512, 1024]) torch.Size([1, 6, 256, 512]) torch.Size([1, 6, 4, 4]) torch.Size([1, 6, 1, 256, 512])
Visualising Frames of Single Video¶
In [15]:
def visualize_sequence(sequence_tensor, rows=2, cols=3):
sequence_length = sequence_tensor.size(0)
# Create subplots
fig, axs = plt.subplots(rows, cols, figsize=(20, 10))
fig.subplots_adjust(hspace=0.1, wspace=0.1)
for i in range(rows * cols):
ax = axs[i // cols, i % cols]
if i < sequence_length:
img_tensor = sequence_tensor[i]
img = np.transpose(img_tensor.numpy(), (1, 2, 0))
ax.imshow(img)
ax.set_title(f"Frame {i+1}", fontsize=10)
ax.axis('off')
plt.tight_layout()
plt.show()
In [16]:
print("Corrupted Carla Images")
for org_batch_idx, (org_sequence_tensors, org_optical_flow, org_segmentation_tensors, org_extrinsics, org_depth_tensor) in enumerate(org_train_loader):
if org_batch_idx == 1: # Choose the second sequence by setting the index to 1, or any other index you wish to visualize
org_first_sequence_tensor = org_sequence_tensors[0]
# Check if the tensor contains non-zero data
if torch.any(org_first_sequence_tensor):
# Now visualize this sequence
visualize_sequence(org_first_sequence_tensor, rows=2, cols=3)
else:
print(f"The tensor for sequence at index {batch_idx} contains only zeros or is not loaded correctly.")
break
print("Base Carla Images")
for batch_idx, (sequence_tensors, optical_flow, segmentation_tensors, extrinsics, depth_tensor) in enumerate(train_loader):
if batch_idx == 1: # Choose the second sequence by setting the index to 1, or any other index you wish to visualize
first_sequence_tensor = sequence_tensors[0]
# Check if the tensor contains non-zero data
if torch.any(first_sequence_tensor):
# Now visualize this sequence
visualize_sequence(first_sequence_tensor, rows=2, cols=3)
else:
print(f"The tensor for sequence at index {batch_idx} contains only zeros or is not loaded correctly.")
break
Corrupted Carla Images
Base Carla Images
In [17]:
# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
cuda
Model's Utilities¶
In [18]:
def decode_segmap(label_mask, n_classes=23):
"""
Decode segmentation class labels into a color image.
Args:
label_mask (numpy.ndarray): an (M,N) array of integer values denoting
the class label at each spatial location.
n_classes (int): the number of classes in the dataset.
Returns:
(numpy.ndarray): the resulting decoded color image.
"""
label_colors = np.array([
(0, 0, 0), # Unlabeled
(70, 70, 70), # Building
(100, 40, 40), # Fence
(55, 90, 80), # others
(220, 20, 60), # Pedestrian
(153, 153, 153), # Pole
(157, 234, 50), # RoadLine
(128, 64, 128), # Road
(244, 35, 232), # SideWalk
(107, 142, 35), # Vegetation
(0, 0, 142), # Vehicles
(102, 102, 156), # Wall
(220, 220, 0), # TrafficSign
(70, 130, 180), # Sky
(81, 0, 81), # Ground
(150, 100, 100), # Bridge
(230, 150, 140), # RailTrack
(180, 165, 180), # GuardRail
(250, 170, 30), # TrafficLight
(110, 190, 160), # Static
(170, 120, 50), # Dynamic
(45, 60, 150), # Water
(145, 170, 100), # Terrain
(255, 255, 255), # ignore_index=22
])
r = np.zeros_like(label_mask).astype(np.uint8)
g = np.zeros_like(label_mask).astype(np.uint8)
b = np.zeros_like(label_mask).astype(np.uint8)
for l in range(0, n_classes):
idx = label_mask == l
r[idx] = label_colors[l, 0]
g[idx] = label_colors[l, 1]
b[idx] = label_colors[l, 2]
rgb = np.stack([r, g, b], axis=2)
return rgb
In [19]:
def calculate_mAcc(preds, targets):
max_preds = preds.argmax(dim=1, keepdim=True)
correct_pred = max_preds.squeeze(1).eq(targets)
acc_per_class = []
for class_idx in range(targets.max() + 1):
correct = correct_pred[targets == class_idx].sum()
total = (targets == class_idx).sum()
if total != 0:
acc_per_class.append(correct.float() / total)
mAcc = torch.mean(torch.stack(acc_per_class)) if acc_per_class else torch.tensor(0)
return mAcc.item()
def calculate_mIoU(predicted_classes, target_classes, num_classes, device='cpu'):
intersection = torch.zeros(num_classes, device=device)
union = torch.zeros(num_classes, device=device)
for class_idx in range(num_classes):
intersection_mask = (predicted_classes == class_idx) & (target_classes == class_idx)
union_mask = (predicted_classes == class_idx) | (target_classes == class_idx)
intersection[class_idx] = intersection_mask.sum()
union[class_idx] = union_mask.sum()
# Avoid division by zero
union[union == 0] = 1
iou = intersection / union
mIoU = iou.mean().item()
return mIoU
In [20]:
def calculate_distances(poses):
# Calculate distances between consecutive poses
distances = torch.sqrt(torch.sum((poses[0, 1:, :3, 3] - poses[0, :-1, :3, 3]) ** 2, axis=1))
return distances.cpu().detach().numpy()
def analyze_translations(original_poses, predicted_poses):
# Compute statistics
original_distances = calculate_distances(original_poses)
predicted_distances = calculate_distances(predicted_poses)
# Compute statistics
statistics = {
"original": {
"mean_distance": np.mean(original_distances),
"std_distance": np.std(original_distances),
"max_distance": np.max(original_distances),
"min_distance": np.min(original_distances),
},
"predicted": {
"mean_distance": np.mean(predicted_distances),
"std_distance": np.std(predicted_distances),
"max_distance": np.max(predicted_distances),
"min_distance": np.min(predicted_distances),
}
}
return statistics
In [21]:
%run EncoderCNN.ipynb
%run ConvRNN.ipynb
%run Decoder.ipynb
%run MotionEncoder.ipynb
%run CameraEncoder.ipynb
%run RNN.ipynb
%run CameraDecoder.ipynb
%run DepthMap.ipynb
Initializing All Components of Architecture¶
In [42]:
# Encoder
encoder = Encoder()
print(encoder)
# ConvLSTM parameters
input_channels = 64
hidden_channels = 64
kernel_size = 3
stride = 1
conv_lstm_model = ConvLSTM(in_channels=input_channels,
hidden_channels=hidden_channels,
kernel_size=kernel_size,
stride=stride,)
print(conv_lstm_model)
# Depth Decoder
depth_decoder = DepthDecoder(input_channels=64, depth_channels=1)
print(depth_decoder)
# Decoder Parameters:
num_classes =23
in_channels = 64
out_channels = 23
decoder = Decoder(in_channels, out_channels)
print(decoder)
# Motion Encoder
motion_in_channels = 66
motion_hidden_channels = 64
motion_kernel_size = 3
motion_encoder = MotionEncoder(motion_in_channels, motion_hidden_channels, motion_kernel_size)
print(motion_encoder)
# Camera Encoder
camera_encoder = CameraEncoder(input_channels=64)
print(camera_encoder)
# RNN
hidden_size = 64
output_size = 16
ego_motion_rnn = EgoMotionRNN(hidden_size, output_size)
print(ego_motion_rnn)
# Camera Decoder
camera_decoder = CameraDecoder(input_size=16, hidden_size=36)
print(camera_decoder)
Encoder(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
)
ConvLSTM(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
DepthDecoder(
(upsample1): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(upsample2): ConvTranspose2d(32, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(depth_conv): Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
Decoder(
(up1): Sequential(
(0): Upsample(scale_factor=2.0, mode='bilinear')
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
(up2): Sequential(
(0): Upsample(scale_factor=2.0, mode='bilinear')
(1): DoubleConv(
(double_conv): Sequential(
(0): Conv2d(32, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(23, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(23, 23, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(4): BatchNorm2d(23, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
)
)
)
)
MotionEncoder(
(conv): Conv2d(66, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
CameraEncoder(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(64, 6, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(adjust_pool): AdaptiveAvgPool2d(output_size=(4, 4))
)
EgoMotionRNN(
(rnn): LSTM(32, 64, batch_first=True)
(fc): Linear(in_features=64, out_features=16, bias=True)
)
CameraDecoder(
(fc1): Linear(in_features=16, out_features=36, bias=True)
(fc2): Linear(in_features=36, out_features=96, bias=True)
(relu): ReLU()
)
In [23]:
import torch.nn as nn
# Optimizer
optimizer = optim.Adam(list(encoder.parameters()) + list(conv_lstm_model.parameters()) + list(depth_decoder.parameters()) + list(decoder.parameters()) + list(motion_encoder.parameters()) + list(camera_encoder.parameters()) + list(ego_motion_rnn.parameters())+ list(camera_decoder.parameters()), lr=3e-4)
#Criterions
criterion = nn.CrossEntropyLoss(ignore_index=22)
Cam_criterion = nn.MSELoss()
def Depth_criterian(predicted, true):
min_value = 1e-8
predicted_clamped = torch.clamp(predicted, min=min_value)
true_clamped = torch.clamp(true, min=min_value)
log_predicted = torch.log(predicted_clamped)
log_true = torch.log(true_clamped)
loss = F.l1_loss(log_predicted, log_true)
return loss
In [24]:
torch.cuda.empty_cache()
Training Loop¶
In [25]:
class SegmentationModelTrainer:
def __init__(self, encoder, conv_lstm, depth_decoder, decoder, motion_encoder, camera_encoder, ego_motion_rnn, camera_decoder, train_loader, val_loader, criterion, Cam_criterion, Depth_criterian, optimizer, device):
self.encoder = encoder
self.conv_lstm_model = conv_lstm
self.depth_decoder = depth_decoder
self.decoder = decoder
self.motion_encoder = motion_encoder
self.camera_encoder = camera_encoder
self.ego_motion_rnn = ego_motion_rnn
self.camera_decoder = camera_decoder
self.train_loader = train_loader
self.val_loader = val_loader
self.criterion = criterion
self.Cam_criterion = Cam_criterion
self.Depth_criterian = Depth_criterian
self.optimizer = optimizer
self.device = device
# Move models to the specified device
self.encoder.to(device)
self.conv_lstm_model.to(device)
self.depth_decoder.to(device)
self.decoder.to(device)
self.motion_encoder.to(device)
self.camera_encoder.to(device)
self.ego_motion_rnn.to(device)
self.camera_decoder.to(device)
def train_epoch(self, epoch):
self.encoder.train()
self.conv_lstm_model.train()
self.depth_decoder.train()
self.decoder.train()
self.motion_encoder.train()
self.camera_encoder.train()
self.ego_motion_rnn.train()
self.camera_decoder.train()
seg_losses = []
depth_losses = []
cam_losses = []
total_losses = []
previous_features = None
for sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor in tqdm(self.train_loader, desc="Training"):
sequence_batch = sequence_batch.to(self.device)
optical_flow_batch = optical_flow_batch.to(self.device)
segmentation_targets = segmentation_targets.to(self.device)
extrinsics = extrinsics.to(self.device)
depth_tensor = depth_tensor.to(self.device)
sequence_length = sequence_batch.size(1)
batch_size = sequence_batch.size(0)
depth_tensor = depth_tensor[0, :, :, :, :]
# Process segmentation targets as needed before using them as model inputs
segmentation_targets = self._process_segmentation_targets(segmentation_targets)
self.optimizer.zero_grad()
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = sequence_batch[:, t, :, :, :]
optical_flow_t = optical_flow_batch[:, t, :, :, :]
# ENCODER
encoder_output_t = self.encoder(img_t)
encoder_outputs.append(encoder_output_t)
# MOTION ENCODER
motion_features_t = self.motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
# After processing all time steps, stack the outputs
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoder = torch.stack(motion_features_list, dim=1)
# Camera Encoder
motion_encoder = motion_encoder[0, :, :, :, :]
camera_encoder_output_t = self.camera_encoder(motion_encoder)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = self.ego_motion_rnn(combined_input)
# CAMERA DECODER
ego_motion_output = ego_motion_output[:, -1, :]
camera_poses = self.camera_decoder(ego_motion_output)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(extrinsics, camera_poses)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_poses.clone()
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = self.conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = self.conv_lstm_model(encoder_output, (hidden_state.to(self.device), cell_state.to(self.device)))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
# DEPTH DECODER
depth_decoder_output = self.depth_decoder(conv_lstm_output)
# Decoder
decoder_output = self.decoder(conv_lstm_output)
# Backpropagation for Segmentation Loss
seg_loss = self.criterion(decoder_output, segmentation_targets)
seg_losses.append(seg_loss.item())
# Backpropagation for Depth Loss
depth_loss = self.Depth_criterian(depth_decoder_output, depth_tensor)
depth_losses.append(depth_loss.item())
# Backpropagation for Camera Pose Loss
cam_loss = self.Cam_criterion(camera_poses_scaled, extrinsics)
cam_losses.append(cam_loss.item())
# Total Losses
total_loss = seg_loss + depth_loss + cam_loss
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
total_losses.append(total_loss.item())
#writer.add_scalar('Loss/Train/Segmentation', seg_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
#writer.add_scalar('Loss/Train/Depth', depth_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
#writer.add_scalar('Loss/Train/Camera', cam_loss.item(), global_step=epoch * len(self.train_loader) + batch_idx)
# Save all models here
model_save_path = 'Models/BaseCARLA'
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
torch.save(self.encoder.state_dict(), os.path.join(model_save_path, f'Encoder.pth'))
torch.save(self.conv_lstm_model.state_dict(), os.path.join(model_save_path, f'ConvLSTM.pth'))
torch.save(self.depth_decoder.state_dict(), os.path.join(model_save_path, f'DepthDecoder.pth'))
torch.save(self.decoder.state_dict(), os.path.join(model_save_path, f'SegmentationDecoder.pth'))
torch.save(self.motion_encoder.state_dict(), os.path.join(model_save_path, f'MotionEncoder.pth'))
torch.save(self.camera_encoder.state_dict(), os.path.join(model_save_path, f'CameraEncoder.pth'))
torch.save(self.ego_motion_rnn.state_dict(), os.path.join(model_save_path, f'EgoMotionRNN.pth'))
torch.save(self.camera_decoder.state_dict(), os.path.join(model_save_path, f'CameraDecoder.pth'))
# converting lists to tensors to calculate means
seg_losses_tensor = torch.tensor(seg_losses)
depth_losses_tensor = torch.tensor(depth_losses)
cam_losses_tensor = torch.tensor(cam_losses)
total_losses_tensor = torch.tensor(total_losses)
# Calculating means
mean_seg_loss = torch.mean(seg_losses_tensor).item()
mean_depth_loss = torch.mean(depth_losses_tensor).item()
mean_cam_loss = torch.mean(cam_losses_tensor).item()
mean_total_loss = torch.mean(total_losses_tensor).item()
return mean_seg_loss, mean_depth_loss, mean_cam_loss, mean_total_loss
@torch.no_grad()
def validate_epoch(self, epoch):
self.encoder.eval()
self.conv_lstm_model.eval()
self.depth_decoder.eval()
self.decoder.eval()
self.motion_encoder.eval()
self.camera_encoder.eval()
self.ego_motion_rnn.eval()
self.camera_decoder.eval()
seg_val_losses = []
cam_val_losses = []
depth_val_losses = []
val_total_losses = []
mAccs = []
val_mIoUs = []
previous_features = None
for val_sequence_batch, val_optical_flow_batch, val_segmentation_targets, val_extrinsics, val_depth_tensor in tqdm(self.val_loader, desc="Validation"):
val_sequence_batch = val_sequence_batch.to(self.device)
val_segmentation_targets = val_segmentation_targets.to(self.device)
val_optical_flow_batch = val_optical_flow_batch.to(self.device)
val_extrinsics = val_extrinsics.to(self.device)
val_depth_tensor = val_depth_tensor.to(self.device)
# Flatten the sequence and batch dimensions
sequence_length = val_sequence_batch.size(1)
batch_size = val_sequence_batch.size(0)
val_depth_tensor = val_depth_tensor[0, :, :, :, :]
# Process segmentation targets as needed before using them as model inputs
val_segmentation_targets = self._process_segmentation_targets(val_segmentation_targets)
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = val_sequence_batch[:, t, :, :, :]
optical_flow_t = val_optical_flow_batch[:, t, :, :, :]
# ENCODER
encoder_output_t = self.encoder(img_t)
encoder_outputs.append(encoder_output_t)
# MOTION ENCODER
motion_features_t = self.motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
# After processing all time steps, stack the outputs
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoder = torch.stack(motion_features_list, dim=1)
# Camera Encoder
motion_encoder = motion_encoder[0, :, :, :, :]
camera_encoder_output_t = self.camera_encoder(motion_encoder)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = val_extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = self.ego_motion_rnn(combined_input)
# CAMERA DECODER
ego_motion_output = ego_motion_output[:, -1, :]
camera_poses = self.camera_decoder(ego_motion_output)
#camera_poses = camera_poses.view(-1, 4, 4)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(val_extrinsics, camera_poses)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_poses.clone()
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = self.conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = self.conv_lstm_model(encoder_output, (hidden_state.to(self.device), cell_state.to(self.device)))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
# DEPTH DECODER
depth_decoder_output = self.depth_decoder(conv_lstm_output)
# Decoder
decoder_output = self.decoder(conv_lstm_output)
mAcc = calculate_mAcc(decoder_output, val_segmentation_targets)
mAccs.append(mAcc)
predicted_classes = torch.argmax(decoder_output, dim=1)
mIoU = calculate_mIoU(predicted_classes, val_segmentation_targets, num_classes, self.device)
val_mIoUs.append(mIoU)
seg_val_loss = self.criterion(decoder_output, val_segmentation_targets)
seg_val_losses.append(seg_val_loss.item())
depth_val_loss = self.Depth_criterian(depth_decoder_output, val_depth_tensor)
depth_val_losses.append(depth_val_loss.item())
cam_val_loss = self.Cam_criterion(camera_poses_scaled, val_extrinsics)
cam_val_losses.append(cam_val_loss.item())
val_total_loss = seg_val_loss + depth_val_loss + cam_val_loss
val_total_losses.append(val_total_loss.item())
# After the loop, convert lists to tensors to calculate means
seg_val_losses_tensor = torch.tensor(seg_val_losses)
depth_val_losses_tensor = torch.tensor(depth_val_losses)
cam_val_losses_tensor = torch.tensor(cam_val_losses)
mAccs_tensor = torch.tensor(mAccs)
val_mIoUs_tensor = torch.tensor(val_mIoUs)
val_total_losses_tensor = torch.tensor(val_total_losses)
# Calculate means
mean_seg_val_loss = torch.mean(seg_val_losses_tensor).item()
mean_depth_val_loss = torch.mean(depth_val_losses_tensor).item()
mean_cam_val_loss = torch.mean(cam_val_losses_tensor).item()
mean_mAcc = torch.mean(mAccs_tensor).item()
mean_val_mIoU = torch.mean(val_mIoUs_tensor).item()
mean_val_total_loss = torch.mean(val_total_losses_tensor).item()
return mean_seg_val_loss, mean_depth_val_loss, mean_cam_val_loss, mean_mAcc, mean_val_mIoU, mean_val_total_loss
def fit(self, epochs):
for epoch in range(epochs):
print(f"Epoch {epoch+1}/{epochs}")
mean_seg_losses, mean_depth_losses, mean_cam_losses, mean_total_loss= self.train_epoch(epochs)
seg_loss_list.append(mean_seg_losses)
depth_loss_list.append(mean_depth_losses)
cam_loss_list.append(mean_cam_losses)
total_loss_list.append(mean_total_loss)
#print(f"Epoch {epoch} - Segmentation Loss: {mean_seg_losses}, Depth Loss: {mean_depth_losses}, Camera Pose Loss: {mean_cam_losses}")
mean_seg_val_loss, mean_depth_val_loss, mean_cam_val_loss, mean_mAcc, mean_val_mIoU, mean_val_total_loss= trainer.validate_epoch(epochs)
seg_val_loss_list.append(mean_seg_val_loss)
depth_val_loss_list.append(mean_depth_val_loss)
cam_val_loss_list.append(mean_cam_val_loss)
mAcc_list.append(mean_mAcc)
val_mIoU_list.append(mean_val_mIoU)
val_total_loss_list.append(mean_val_total_loss)
print(f"Epoch {epoch} - Val Segmentation Loss: {mean_seg_val_loss}, Val Depth Loss: {mean_depth_val_loss}, Val Camera Pose Loss: {mean_cam_val_loss}")
print(f"Epoch {epoch} - Val mAcc: {mean_mAcc}, Val mIoU: {mean_val_mIoU}")
def _process_segmentation_targets(self, segmentation_targets):
# Preprocessing on segmentation_targets
if segmentation_targets.size()[-1] == 3:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, :, :, 0]
segmentation_targets = segmentation_targets.long()
elif segmentation_targets.dim() == 4:
segmentation_targets= segmentation_targets[0, :, :, :]
else:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, 0, :, :]
segmentation_targets = segmentation_targets.long()
return segmentation_targets
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#device = torch.device('cpu')
# Initialize the Trainer
trainer = SegmentationModelTrainer(encoder, conv_lstm_model, depth_decoder, decoder, motion_encoder, camera_encoder, ego_motion_rnn, camera_decoder, train_loader, val_loader, criterion, Cam_criterion, Depth_criterian, optimizer, device)
# Train the model
val_mIoU_list = []
mAcc_list = []
seg_loss_list, depth_loss_list, cam_loss_list, total_loss_list = [], [], [], []
seg_val_loss_list, depth_val_loss_list, cam_val_loss_list, val_total_loss_list= [], [], [], []
epochs=5
trainer.fit(epochs)
writer.close()
Epoch 1/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:44:20<00:00, 1.10s/it] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [25:08<00:00, 1.01s/it]
Epoch 0 - Val Segmentation Loss: 2.309443712234497, Val Depth Loss: 17.63500213623047, Val Camera Pose Loss: 3514.765625 Epoch 0 - Val mAcc: 0.11580894142389297, Val mIoU: 0.05871982127428055 Epoch 2/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:40:58<00:00, 1.08s/it] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [19:29<00:00, 1.28it/s]
Epoch 1 - Val Segmentation Loss: 2.027698516845703, Val Depth Loss: 0.40556392073631287, Val Camera Pose Loss: 3731.859619140625 Epoch 1 - Val mAcc: 0.12786030769348145, Val mIoU: 0.07034921646118164 Epoch 3/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [1:53:00<00:00, 1.32it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [21:18<00:00, 1.17it/s]
Epoch 2 - Val Segmentation Loss: 2.032072067260742, Val Depth Loss: 0.3734437823295593, Val Camera Pose Loss: 2372.5029296875 Epoch 2 - Val mAcc: 0.13229341804981232, Val mIoU: 0.07438379526138306 Epoch 4/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:07:35<00:00, 1.17it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [20:42<00:00, 1.20it/s]
Epoch 3 - Val Segmentation Loss: 1.9531418085098267, Val Depth Loss: 0.3673743009567261, Val Camera Pose Loss: 2518.62939453125 Epoch 3 - Val mAcc: 0.13382981717586517, Val mIoU: 0.07588988542556763 Epoch 5/5
Training: 100%|███████████████████████████████████████████████████████████████████| 8982/8982 [2:10:45<00:00, 1.14it/s] Validation: 100%|███████████████████████████████████████████████████████████████████| 1497/1497 [28:54<00:00, 1.16s/it]
Epoch 4 - Val Segmentation Loss: 2.0047407150268555, Val Depth Loss: 0.35936471819877625, Val Camera Pose Loss: 4298.54931640625 Epoch 4 - Val mAcc: 0.14035050570964813, Val mIoU: 0.08064104616641998
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[25], line 362 360 epochs=5 361 trainer.fit(epochs) --> 362 writer.close() NameError: name 'writer' is not defined
Visualising Accuracies and Losses¶
In [ ]:
# Aggregating your training and validation statistics
training_stats = {
"seg_loss_list": seg_loss_list,
"depth_loss_list": depth_loss_list,
"cam_loss_list": cam_loss_list,
"total_loss_list": total_loss_list,
"seg_val_loss_list": seg_val_loss_list,
"depth_val_loss_list": depth_val_loss_list,
"cam_val_loss_list": cam_val_loss_list,
"val_total_loss_list": val_total_loss_list,
"mAcc_list": mAcc_list,
"val_mIoU_list": val_mIoU_list,
}
# Saving the statistics to a JSON file
with open('Models/BaseCARLA/training_stats.json', 'w') as f:
json.dump(training_stats, f, indent=4)
print("Training statistics saved to 'Models/BaseCARLA/training_stats.json'.")
In [3]:
import json
with open('Models/BaseCARLA/training_stats.json', 'r') as f:
training_stats = json.load(f)
epochs = range(1, len(training_stats['seg_loss_list']) + 1)
plt.figure(figsize=(14, 10))
# Mean Accuracy (mAcc)
plt.subplot(2, 3, 1)
plt.plot(epochs, training_stats['mAcc_list'], label='Mean Accuracy (mAcc)')
plt.plot(epochs, training_stats['val_mIoU_list'], label='Mean IoU (mIoU)', linestyle='--')
plt.xlabel('Epoch')
plt.ylabel('mAcc')
plt.title('Mean Accuracy Over Epochs')
plt.legend()
# Segmentation Losses
#plt.figure(figsize=(14, 10))
plt.subplot(2, 3, 2)
plt.plot(epochs, training_stats['seg_loss_list'], label='Segmentation Loss')
plt.plot(epochs, training_stats['seg_val_loss_list'], label='Validation Segmentation Loss', linestyle='--')
plt.title('Segmentation Losses')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
# Depth Losses
plt.subplot(2, 3, 3)
plt.plot(epochs, training_stats['depth_loss_list'], label='Depth Loss')
plt.plot(epochs, training_stats['depth_val_loss_list'], label='Validation Depth Loss', linestyle='--')
plt.title('Depth Losses')
plt.xlabel('Epoch')
plt.legend()
# Camera Pose Losses
plt.subplot(2, 3, 4)
plt.plot(epochs, training_stats['cam_loss_list'], label='Camera Pose Loss')
plt.plot(epochs, training_stats['cam_val_loss_list'], label='Validation Camera Pose Loss', linestyle='--')
plt.title('Camera Pose Losses')
plt.xlabel('Epoch')
plt.legend()
# Combined Model Losses
plt.subplot(2, 3, 5)
plt.plot(epochs, training_stats['total_loss_list'], label='Combined Model Loss')
plt.plot(epochs, training_stats['val_total_loss_list'], label='Validation Combined Model Loss', linestyle='--')
plt.title('Overall Model Losses')
plt.xlabel('Epoch')
plt.legend()
plt.tight_layout()
plt.show()
Reloading Saved Models and Visualising Results¶
In [43]:
model_load_path = 'Models/BaseCARLA'
encoder.load_state_dict(torch.load(os.path.join(model_load_path, 'Encoder.pth'), map_location=device))
conv_lstm_model.load_state_dict(torch.load(os.path.join(model_load_path, 'ConvLSTM.pth'), map_location=device))
depth_decoder.load_state_dict(torch.load(os.path.join(model_load_path, 'DepthDecoder.pth'), map_location=device))
decoder.load_state_dict(torch.load(os.path.join(model_load_path, 'SegmentationDecoder.pth'), map_location=device))
motion_encoder.load_state_dict(torch.load(os.path.join(model_load_path, 'MotionEncoder.pth'), map_location=device))
camera_encoder.load_state_dict(torch.load(os.path.join(model_load_path, 'CameraEncoder.pth'), map_location=device))
ego_motion_rnn.load_state_dict(torch.load(os.path.join(model_load_path, 'EgoMotionRNN.pth'), map_location=device))
camera_decoder.load_state_dict(torch.load(os.path.join(model_load_path, 'CameraDecoder.pth'), map_location=device))
Out[43]:
<All keys matched successfully>
In [44]:
# Function show all frames
def visualize_sequence_full(sequence_batch, segmentation_targets, predicted_segmentation, depth_tensor, predicted_depth, device):
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
fig, axes = plt.subplots(1, 5, figsize=(25, 5))
# Input image
axes[0].imshow(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
axes[0].set_title('Input Image')
# Ground truth segmentation
gt_segmap = decode_segmap(segmentation_targets[0, i].cpu().numpy())
axes[1].imshow(gt_segmap)
axes[1].set_title('Ground Truth Segmentation')
# Predicted segmentation
pred_segmap = decode_segmap(predicted_segmentation[i].cpu().numpy())
axes[2].imshow(pred_segmap)
axes[2].set_title('Predicted Segmentation')
# Ground truth depth map
axes[3].imshow(depth_tensor[0, i].squeeze().cpu().numpy(), cmap='gray')
axes[3].set_title('Ground Truth Depth Map')
# Predicted depth map
axes[4].imshow(predicted_depth[0, i].squeeze().cpu().detach().numpy(), cmap='gray')
axes[4].set_title('Predicted Depth Map')
plt.show()
In [45]:
# After loading all the models
encoder.eval()
conv_lstm_model.eval()
depth_decoder.eval()
decoder.eval()
motion_encoder.eval()
camera_encoder.eval()
ego_motion_rnn.eval()
camera_decoder.eval()
Out[45]:
CameraDecoder( (fc1): Linear(in_features=16, out_features=36, bias=True) (fc2): Linear(in_features=36, out_features=96, bias=True) (relu): ReLU() )
In [46]:
import imageio
import numpy as np
import torch
import os
# Make sure the save_gif function converts images to uint8
def save_gif(image_sequence, filename, duration=10):
with imageio.get_writer(filename, mode='I', duration=duration, loop=0) as writer:
for image in image_sequence:
# Convert to uint8 if not already
if image.dtype != np.uint8:
# Assume the image data is in float and scale accordingly
image = (255.0 * image).astype(np.uint8)
writer.append_data(image)
In [47]:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plot_camera_trajectories(original_poses, predicted_poses):
# Extract the translation components from original and predicted poses
original_translations = original_poses[0, :, :3, 3].cpu().detach().numpy()
predicted_translations = predicted_poses[0, :, :3, 3].cpu().detach().numpy()
# Create a 3D plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Plot the original trajectory
ax.plot(original_translations[:, 0], original_translations[:, 1], original_translations[:, 2],
marker='o', color='red', label='Original Poses')
# Plot the predicted trajectory
ax.plot(predicted_translations[:, 0], predicted_translations[:, 1], predicted_translations[:, 2],
marker='^', color='blue', label='Predicted Poses')
# Label each pose in the original trajectory
for i, t in enumerate(original_translations):
ax.text(t[0], t[1], t[2], f'O{i}', color='red')
# Label each pose in the predicted trajectory
for i, t in enumerate(predicted_translations):
ax.text(t[0], t[1], t[2], f'P{i}', color='blue')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Camera Pose Trajectories')
ax.legend()
plt.show()
In [49]:
device = torch.device('cpu')
GIF_path = "GIF/BaseCARLA"
def _process_segmentation_targets(segmentation_targets):
# Preprocessing on segmentation_targets
if segmentation_targets.size()[-1] == 3:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, :, :, 0]
segmentation_targets = segmentation_targets.long()
elif segmentation_targets.dim() == 4:
segmentation_targets= segmentation_targets[0, :, :, :]
else:
batch_size, seq_len, _, height, width = segmentation_targets.size()
segmentation_targets= segmentation_targets[0, :, 0, :, :]
# Convert segmentation_targets to Long type
segmentation_targets = segmentation_targets.long()
return segmentation_targets
for batch in val_loader:
# Move batch to device
sequence_batch, optical_flow_batch, segmentation_targets, extrinsics, depth_tensor = [item.to(device) for item in batch]
seg_targ = segmentation_targets
sequence_batch = sequence_batch
dep_targ = depth_tensor
optical_flow_batch = optical_flow_batch
segmentation_targets = segmentation_targets
extrinsics = extrinsics
depth_tensor = depth_tensor
sequence_length = sequence_batch.size(1)
batch_size = sequence_batch.size(0)
depth_tensor = depth_tensor[0, :, :, :, :]
# Process segmentation targets as needed before using them as model inputs
segmentation_targets = _process_segmentation_targets(segmentation_targets)
optimizer.zero_grad()
encoder_outputs = []
motion_features_list = []
for t in range(sequence_length):
img_t = sequence_batch[:, t, :, :, :]
optical_flow_t = optical_flow_batch[:, t, :, :, :]
# ENCODER
encoder_output_t = encoder(img_t)
encoder_outputs.append(encoder_output_t)
# MOTION ENCODER
motion_features_t = motion_encoder(encoder_output_t, optical_flow_t)
motion_features_list.append(motion_features_t)
# After processing all time steps, stack the outputs
encoder_output = torch.stack(encoder_outputs, dim=1)
motion_encoderr = torch.stack(motion_features_list, dim=1)
# Camera Encoder
motion_encoderr = motion_encoderr[0, :, :, :, :]
camera_encoder_output_t = camera_encoder(motion_encoderr)
# RNN
# Doing dimentionality reduction
camera_encoder_output_t = camera_encoder_output_t.mean(dim=0, keepdim=True)
camera_encoder_output_flat = camera_encoder_output_t.view(batch_size, sequence_length, -1)
extrinsics_flat = extrinsics.view(batch_size, sequence_length, -1)
combined_input = torch.cat((camera_encoder_output_flat, extrinsics_flat), dim=2)
ego_motion_output = ego_motion_rnn(combined_input)
# CAMERA DECODER
ego_motion_output = ego_motion_output[:, -1, :]
camera_poses = camera_decoder(ego_motion_output)
# Calculate the scaling factor based on the means of the original and predicted distances
stats = analyze_translations(extrinsics, camera_poses)
scaling_factor = stats['original']['mean_distance'] / stats['predicted']['mean_distance']
# Apply the scaling factor to the translations of the predicted camera poses
camera_poses_scaled = camera_poses.clone() # Clone to avoid modifying the original poses
camera_poses_scaled[:, :, :3, 3] *= scaling_factor
# Hidden Layer_org
batch_size, seq_length, channels, height, width = encoder_output.size()
hidden_state, cell_state = conv_lstm_model.init_hidden(batch_size, height, width)
# ConvLSTM
conv_lstm_output, (hidden_state, cell_state) = conv_lstm_model(encoder_output, (hidden_state, cell_state))
batch_size, seq_length, channels, height, width = conv_lstm_output.size()
conv_lstm_output = conv_lstm_output.view(batch_size * seq_length, channels, height, width)
# DEPTH DECODER
depth_decoder_output = depth_decoder(conv_lstm_output)
# Decoder
decoder_output = decoder(conv_lstm_output)
_, predicted_segmentation_labels = torch.max(decoder_output, dim=1)
seq, chan, h, w = depth_decoder_output.size()
depth_output = depth_decoder_output.view(batch_size, seq, h, w)
#print(segmentation_targets.shape)
#seg_target= segmentation_targets[0, :, :]
visualize_sequence_full(
sequence_batch,
seg_targ,
predicted_segmentation_labels,
dep_targ,
depth_output,
device
)
# GIFs Code
input_images_sequence = []
gt_segmentation_sequence = []
predicted_segmentation_sequence = []
gt_depth_maps_sequence = []
predicted_depth_maps_sequence = []
batch_size, sequence_length, c, h, w = sequence_batch.size()
for i in range(sequence_length):
input_images_sequence.append(sequence_batch[0, i].permute(1, 2, 0).cpu().numpy())
gt_segmap = decode_segmap(seg_targ[0, i].cpu().numpy())
gt_segmentation_sequence.append(gt_segmap)
pred_segmap = decode_segmap(predicted_segmentation_labels[i].cpu().numpy())
predicted_segmentation_sequence.append(pred_segmap)
gt_depth_map = dep_targ[0, i].squeeze().cpu().numpy()
if np.max(gt_depth_map) > 255 or np.min(gt_depth_map) < 0:
gt_depth_map = (gt_depth_map - np.min(gt_depth_map)) / (np.max(gt_depth_map) - np.min(gt_depth_map)) # Normalizing to 0-1
gt_depth_maps_sequence.append(gt_depth_map)
predicted_depth_map = depth_output[0, i].squeeze().cpu().detach().numpy()
if np.max(predicted_depth_map) > 255 or np.min(predicted_depth_map) < 0:
predicted_depth_map = (predicted_depth_map - np.min(predicted_depth_map)) / (np.max(predicted_depth_map) - np.min(predicted_depth_map)) # Normalizing to 0-1
predicted_depth_maps_sequence.append(predicted_depth_map)
# Save GIFs
save_gif(input_images_sequence, os.path.join(GIF_path, 'input_images.gif'))
save_gif(gt_segmentation_sequence, os.path.join(GIF_path, 'gt_segmentation.gif'))
save_gif(predicted_segmentation_sequence, os.path.join(GIF_path, 'predicted_segmentation.gif'))
save_gif(gt_depth_maps_sequence, os.path.join(GIF_path, 'gt_depth_maps.gif'), duration=0.2)
save_gif(predicted_depth_maps_sequence, os.path.join(GIF_path, 'predicted_depth_maps.gif'), duration=0.2)
# Camera Poses Code
plot_camera_trajectories(extrinsics, camera_poses_scaled)
break
In [50]:
from IPython.display import HTML
gif_filenames = ['input_images.gif', 'gt_segmentation.gif', 'predicted_segmentation.gif', 'gt_depth_maps.gif', 'predicted_depth_maps.gif']
gif_html = '<table><tr>'
for gif in gif_filenames:
gif_path = os.path.join(GIF_path, gif)
gif_html += f'<td><img src="{gif_path}" alt="{gif}" style="width:160px;height:120px;"></td>'
gif_html += '</tr></table>'
HTML(gif_html)
Out[50]:
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In [ ]:
Training statistics saved to 'Models/BaseCARLA/training_stats.json'.